Senior AI Product Manager

Identify, define, deliver and continuously improve AI products that create measurable business and user value. Translate opportunities into product requirements, features and roadmaps. Guide model and technology choices with specialist teams, establish evaluation and monitoring requirements, and shape product positioning and pricing throughout the product lifecycle.The role spans AI product strategy and product ownership across platforms and use cases. It provides a clear connection between business needs, user experience, technical delivery, responsible AI and commercial outcomes.Activities and responsibilities1 Identify and prioritise opportunitiesInvestigate user needs, business processes and market opportunities. Define the problem, target users, current performance baseline and intended benefits. Assess whether AI is appropriate and prioritise use cases against value, feasibility, data readiness, cost and risk. Develop the business case and investment priorities with stakeholders.2 Develop AI product requirementsOwn the product requirements document for each use case. Specify intended behaviour, inputs and outputs, scope, user journeys, data requirements, integrations, limitations and measurable acceptance criteria. Describe human review, escalation and fallback needs. Translate business expectations into requirements that delivery teams can implement and evaluate.3 Inform model and technology choicesMaintain a practical understanding of the AI landscape and emerging technologies. Work with researchers, ML engineers and architects to assess models and solution approaches against product requirements. Frame trade-offs involving quality, response time, cost, explainability, security and deployment constraints. Request relevant benchmarks and experiments to support decisions and revisit choices as evidence changes.4 Define features and the product roadmapTranslate use cases into clearly articulated capabilities, features and a prioritised backlog. Define what each feature should do and how users will benefit. Build a roadmap covering discovery, experimentation, pilot delivery, release and improvement. Account for data preparation, technical integration and governance dependencies, and adjust scope and sequencing as evidence emerges.Delivery and lifecycle responsibilities5 Specify data requirements and readinessDefine required data sources, formats, coverage, volumes, quality checks and labelling needs with specialist teams. Coordinate access and permitted use with data owners. Identify gaps and preparation dependencies, and incorporate them into delivery estimates and the roadmap. Document relevant data handling constraints for review by the appropriate control teams.6 Establish product and solution evaluationsOwn the evaluation plan with data scientists, ML engineers and domain experts. Define metrics, baselines, targets, test populations, measurement methods and evaluation periods. Include representative, difficult and failure cases. Evaluate individual capabilities and the complete user workflow, including human intervention and downstream effects. Agree acceptance thresholds, release criteria and the evidence needed for each decision.7 Monitor performance and improve the productSpecify telemetry, user feedback and reporting requirements. Review quality, adoption, outcomes, failures and cost over time. Evaluate changes to models, prompts, data and workflows before release. Prioritise improvements based on evidence and coordinate responses when performance falls below agreed thresholds, including escalation or rollback where appropriate.8 Coordinate responsible AI and release readinessEngage governance, risk, security, legal and compliance stakeholders early. Explain the product purpose, functionality, inputs and outputs, data handling, limitations and proposed controls. Incorporate their requirements into the backlog and release process. Coordinate evidence for review and ensure product claims reflect demonstrated capability. Maintain visibility of approvals, operational ownership and unresolved risks.9 Define positioning and pricingDevelop the value proposition and product positioning. Understand the full cost of data, models, infrastructure, integration, operations and human review. Compare relevant subscription, consumption and outcome-based pricing approaches where applicable. Assess cost per successful outcome and sensitivity to usage. For internal products, work with Finance and business owners on transparent consumption measures, cost allocation and transfer-pricing proposals.10 Drive adoption and value realisationAlign business and technical stakeholders around priorities, delivery expectations and product outcomes. Support training, change management and user adoption. Track realised benefits against the business case and review whether the product should expand, change or retire. Manage budget and supplier dependencies with the relevant owners and communicate decisions and progress clearly.Accountability and decision boundariesThe AI Product Manager owns product outcomes, requirements, priorities and the evaluation framework. Researchers, engineers and architects own technical implementation and specialist design decisions within their mandates. Governance and control functions retain their approval responsibilities. Finance and business owners agree pricing and internal charging arrangements with the product manager.Expectations from delivery partnersData engineers: Clear data specifications, quality criteria, access and usage constraints, and priorities for closing readiness gaps.Data scientists: Defined objectives, metric definitions, evaluation questions, representative cases, telemetry and feedback needs.Researchers and ML engineers: Product constraints, testable hypotheses, benchmark priorities, access to evaluation resources and review milestones.Engineering MLOps and design: User journeys, feature behaviour, integration requirements, operational expectations and release acceptance criteria.Governance and control teams: Plain-language product scope, data handling, limitations, proposed controls and evidence for review.Business and Finance: Value case, prioritised roadmap, budget and cost visibility, adoption measures and pricing or cost-recovery proposals.Qualifications and experienceExperience in product management, product discovery, business analysis, or software development lifecycle (SDLC) practices, including requirements elicitation, user story development, and solution designDemonstrated experience in product roadmap development, backlog prioritisation, Agile delivery methodologies, and cross-functional product deliveryFoundational to intermediate knowledge of Artificial Intelligence (AI), Machine Learning (ML), and Generative AI, supported by relevant certifications (e.g., Microsoft AI certifications, Claude Associate, OpenAI, AWS, Google, or equivalent AI credentials)Experience working with data-driven products, including data quality assessment, data governance, data modelling, and ensuring data readiness for analytics and AI use casesExperience designing, managing, or supporting pilots, proof-of-concepts (PoCs), A/B testing, and model evaluation frameworksProficiency in analytics and performance measurement tools (e.g., Power BI, Tableau, Qlik, Azure Monitor, Application Insights, or equivalent) to track business, product, and operational outcomesKnowledge of Responsible AI principles, AI governance frameworks, risk management, regulatory compliance, privacy, and ethical AI practices.Experience developing business cases, financial models, ROI assessments, cost-benefit analyses, and pricing strategies for digital, data, or AI-enabled products and servicesStrong stakeholder engagement experience, with a proven ability to communicate complex technical concepts to business audiences, facilitate decision-making, and support change management and adoption initiativesPreferred QualificationsBachelor's degree in Computer Science, Information Systems, Engineering, Data Science, Business Science, or a related field.Professional certifications in Product Management (PSPO, CSPO, SAFE POPM), Agile Delivery, Business Analysis (CBAP, PMI-PBA), or AI and Data disciplines.Experience delivering AI, analytics, digital transformation, or enterprise technology initiatives within large, complex organisations.